generative-ai on S Anand

AI on flights

Thu, 18 Jun 2026 10:35:32 +0530

I love that I get uninterrupted 4-16 hours on flights, which I mostly use to write future prompts and read past AI responses.

I do miss AI on flights. But after installing Google Edge Gallery with Gemma-4-E2B-it (2.5GB) that runs on my mobile, I’ve solved a few practical problems.

For example:

I took a picture of a dish they served and asked: “Is this vegetarian?” (It was.)
I asked, “Comics have text in panels, often written at the top in a box. Not the speech bubbles. It’s like a narrator or voice over. What are they called?” (Caption boxes.)
“Summarize The Unbearable Lightness of Being. Why is it famous?” (Thoughtful, well-written novel on the choice vs commitment tradeoff.)

It’s not a very smart model. It’s a bit slow. Transcription is average. It doesn’t run in the background. Only one chat at a time. No internet search, etc.

But it’s a good reference to have. Almost a Wikipedia I can talk to.
It’s a good ideator to have. I can brainstorm. (Hallucination is a feature, not bug.)

I’m sure I’ll find more uses.

Oh Shit moments with Gen AI

Sun, 07 Jun 2026 20:13:01 +0530

Hacker News has a lively thread asking What was your “oh shit” moment with GenAI?.

Here are two dozen that gives a sense of what real people find impressive (or worrying) about AI capabilities.

Analysis simonw used ChatGPT Code Interpreter to upload a CSV, analyze it, create charts, automating everything a software for journalists would do.
Analysis Sobrino saw that a months-long OCR project to read and clean-up PDFs is now just a prompt on ChatGPT.
Coding plumefar used Claude and Gemini to modernize 20-30 years of chemistry code in 10 days.
Coding veidr used a multi-agent fleet managing coordination, testing, UI feedback loops, etc. with no-human-in-loop coding to build a useful git-submodule GUI.
Creativity idopmstuff used Nano Banana Pro to turn a poor iPhone product photo into usable e-commerce product photography and Amazon-style infographics, replacing a photographer/designer workflow.
Creativity koreth1 used Suno to generate a K-pop-style anthem about their family dog with a catchy melody and lyrics funny enough to make the family laugh.
Education plagasul saw a teacher automate grading feedback emails based on notes and the student list spreadsheet.
Education aniviacat watched a non-technical brother build a complex working app with Codex using vague, shallow wording despite not knowing code, git, or technical details.
Hardware ivanvanderbyl used Claude to reverse engineer a FujiFilm camera’s Bluetooth/Wi-Fi transfer protocol and build a much faster native Mac/iOS transfer app.
Hardware shreddude had Claude decompile camper van firmware, document CAN interfaces, and program an ESP32 to control power, HVAC, lighting, and tanks.
Health TylerE used Claude as a health adjunct to organize a complex medical profile, screen for drug interactions, log symptoms, and draft portal messages to doctors.
Legal bsiverly used AI to prepare a San Francisco property-tax appeal with valuation research, and the city agreed, sending a $12k refund.
Legal grumblepeet used AI to fill out complex government-framework enrollment forms and identify the certification steps needed, transforming their business.
Personal acosmism used ChatGPT screenshots to understand and operate a 100-year-old home’s steam heating system in winter despite knowing nothing about it.
Personal andrewthornton used Gemini videos to diagnose a broken furnace during a cold holiday weekend and keep it running until HVAC service arrived.
Research angusturner found that Opus does reads papers, does architecture research and creates CUDA kernels… It is AI automating AI research.
Research chaoxu used ChatGPT to find a counterexample to a theoretical computer science conjecture they’d been trying for 2 years.
Research rochansinha built a physics-based digital twin for an electrolyzer system, covering thermodynamics, fluid dynamics, and electrochemical reactions at a level usually needing expensive specialist software.
Security kstrauser used a coding agent to test an open source vulnerability, and in a few minutes, had a tool that could crash any system using this software.
Security raesene9 gave an LLM a Linux privilege-escalation PoC and asked whether it could become a container breakout; it generated a working container breakout in one prompt.
Society laboring1 read that a character.ai chatbot encouraged a child to commit suicide, making the “oh shit” moment about real-world harm, not capability.
Society ozgung realized AI makes large-scale profiling, surveillance, and social-media analysis cheap, fast, and accurate enough to change privacy and power dynamics.
Work binarysolo used Gemini to reverse engineer a departed employees’ work from their emails/docs/calendar/meetings and create an onboarding document.
Work eqmvii built a Slack agent that took over a 30-minute internal business process, handled ambiguity and edits, and eventually killed the old process.

It's who you know

Tue, 02 Jun 2026 09:47:12 +0800

Dharmendra Singh shared how they built an app with AI.

That’s normal. I’m just thrilled they used client transcripts as the source.

Basically, they converted the “voice of the client” to working software. To quote them: “A strong spoken business narrative can be converted into a usable product brief quickly when the capture step is disciplined.”

You know what this means? Interviewing is a skill to hire for. Better questions = better answers = better apps.

… until AI starts interviewing better than us (which it might be already). At that point, picking whom to interview becomes important.

You know what that means? People management is a skill to hire for. Better stakeholders = better interviews = better apps.

… until AI understand people by mining signals better than us (which it might be already). At which point, stuff you can’t capture or express (body language, trust, um… nepotism becomes more important.

You know what that means? It’s who you know, not what you know.

But wait… isn’t there supposed to be something wrong with that?

Sigh… time to review Straive hiring policies.

Things I Learned - 17 May 2026

Sun, 17 May 2026 00:00:00 +0000

This week, I learned:

I had GPT-5.5 and Opus 4.7 analyze a few of my conversations and learnt that I need to ask myself: “What must they take away? What must you take away?” in my conversations. That lets me speak with intention rather than instict. (Instinct has its place. I happen to over-use it.)
Turns out there are several well-established taxonomies. It makes sense to align with these. Linked data is powerful and AI makes linkage easy.
- General Knowledge: Wikidata, DBpedia, YAGO.
- People: VIAF, ISNI, ORCID, LC Name Authority, GND.
- Places: GeoNames, Getty TGN, ISO 3166.
- Organizations: LEI, ROR, Wikidata.
- Books/Media: Open Library, WorldCat, MusicBrainz, IMDB.
- Chemicals/Biology: PubChem, ChEBI, GBIF, ITIS.
- Legal/Units/Math/Events: EuroVoc, QUDT, OEIS, PeriodO, etc.
BitWarden supports a bw CLI that seems handy for quick CLI access to passwords. It’s a step towards me moving away from saving passwords unencrypted on my local file system.
Singapore has banned prediction markets like Polymarket and Kalshi. Pity. I was hoping to use AI coding agents to play them. Yahoo
flipbook.page is a fascinating generative UI exploration. It’s a visual browser, i.e. it generates an image based on text, you click anywhere, it generates an image interpreting based on where you clicked, and so on. A very different style of exploration!
Vercel’s deepsec uses Codex / Claude to search for vulnerabilities, but “scans can cost thousands or even tens-of-thousands of dollars for large codebases”.
When I charge my Lenovo Thinkpad (P1 Gen 7) with the 170W charger that came with the laptop, it delivers ~60W of power to the battery, charging the laptop in about an hour. A 65W laptop delivers half the power and takes twice as long.

Things I Learned - 22 Mar 2026

Sun, 22 Mar 2026 00:00:00 +0000

This week, I learned:

Psychological operations in design by Narendra Ghate
- When lights are dimmed people speak softer. So, dimming lights reduces sound levels in noisy offices.
- Rather than reduce the size of shampoo sachets (which customers and business both hate), include 2 shampoos in one sachet, tearable in the middle.
- Price saches at 95p with a 5p deposit for the sachet - which rag-pickers can collect and return to the retailer.
- People think of stains like wounds on cloth. So a “stain band-aid” where you stick a strip, and remove it after 5 min to remove the stain, is catchy.
- A mechanical wind-up fish that stirs the water in the bucket while clothes are soaking speeds up the process.
Senthil & Amutha, founders of Payir demonstrated a re-usable fabric calendar that converts into a bag for re-use. Pretty clever! Their message at the Chennai Design Festival was that good design can be for the masses and by the masses to reclaim their time, energy, and joy.
The urinary bladder works based on involuntary muscular contractions towards the end, to clear out the last bits of fluid. It’s not fluid flow, it’s muscle contractions. (Oh, the things I learn!) Gemini
Indigo bans ghee in cabin baggage. Also coconuts, pickles, oily foods, gooey cakes, spices (masala, powders), strong-smelling food. ChatGPT
New skill unlocked: how to demo without knowing what you’re demo-ing. STEP 1: Copy-paste all demo pages as Markdown. STEP 2: Tell AI “Here is a demo I’ll be showing. (Add context.) Tell me how I should explain this and what I should point out as specific examples. Use concise bullets.”
We’ve learnt not to do things we don’t know how to (until we learn it). When AI is doing things, this is a bottleneck. Get out of the way. Stop filtering for what YOU can do. Stop learning what IT can do. Ask for it. That’s faster. Learning can come later.
I keep forgetting that QR codes need a white border for them to work.
TerraDraw provides a unified API across multiple mapping libraries. (In the vibe-coding era, this is not as useful.)
To create desktop apps declaratively on Linux, Slint, Flutter, QML(Qt) and GTK4 are options. Slint and Flutter seem to be cross platform. Slint is newer, less mature but compiles to small fast binaries and might be a good option to explore. Flutter seems more mature and fairly popular. Claude
PyTorch Tracing watches one forward pass and freezes the path into a portable recipe. But it silently ignores branches your example didn’t take. Claude
The Internet is forking into a human internet vs an agent web LinkedIn
SamGeo is a Python Package for geospatial image processing. While OlmoEarth provides geospatial embeddings, SamGeo can convert geospatial data to vector data! So you can do things like:
- Create the outer boundary of all apartments with swimming pools in a city
- Extract the shape of all lakes across the years to find out how they’re changing.
Terence started Foundation for Science and AI Research (SAIR) to use AI in science research. Verifiable proofs (e.g. LEAN) are a big part of this.
Since AI needs to run on phones and that needs GPUs, a lot of phones might need replacement in the next few years.

Tue, 23 Dec 2025 05:36:05 +0000

I used to be a data visualization expert. I’m not sure I still am.

When Anthropic published an article about how AI is transforming their engineers’ work, I ran this prompt:

Suggest how the following engineer productivity patterns can be illustrated using interactive animated charts, graphs, or infographics.

Be diverse. Xenographics are welcome. Novel animation* / *interaction styles, artistry, xenographics, and diverse chart types are encouraged.

Be intuitive. A single glance should tell them exactly what insight we are trying to convey.

Its suggestions were better than mine.

Gemini 3 Pro was the best, followed by Claude (Opus/Sonnet 4.5) and then ChatGPT (GPT 5.2). They came up with:

a Throughput Flow showing commits as particles, going from a trickle to a stream
a Knowledge Neural Net where an engineer’s mental model transforms from isolated clusters into a dense mesh
a Dark Matter Radar detecting invisible tasks, a Feedback Pulse, a polyomino task stacking
… and more.

I have ~20 years of professional data visualization experience. I teach it. And these AIs just casually suggested more creative visualization ideas in 10-30 minutes than I probably would have come up with in a day.

This cost less than $2. The new per-day-rate for my expertise, I guess.

Luckily: my expertise has two parts.

Part 1: Ideating innovative visualizations.
Part 2: Selecting the right one for the job.

AI’s better at ideation. It’s fast and can generate creative ideas faster than I can sketch on a whiteboard. It knows xenographics and every visualization ever made and can recombine them in novel ways.

I’m better at selection. For now. Knowing which visualization requires knowing the audience, context, and intent. It requires taste (accumulated wisdom from showing the wrong chart to the wrong audience enough times).

But still, since AI can ideate, anyone can create innovative visualizations. No 20-years of experience, reading Edward Tufte’s books, or comparing a violin plot and a beeswarm. Just tell the story and AI will suggest ten creative ways to tell it. Take your pick.

I’m no longer a bottleneck.

Naveen Gattu - you asked me to clone myself. 10 years later, here’re the recipe: https://sanand0.github.io/datastories/anthropic-work/

Video

Mon, 01 Dec 2025 05:36:05 +0000

I joined Madhu Sathiaseelan’s podcast to talk about LLM Psychology.

But it’s also fascinating to see how much SECONDARY content you can generate from a video.

Do you prefer sketch-notes? See Nano Banana Pro’s version below.

Or are you a slides person? https://sanand0.github.io/talks/2025-11-06-llm-psychology/

How about a Malcolm Gladwell article? https://github.com/sanand0/talks/raw/refs/heads/main/2025-11-06-llm-psychology/mind-readers.docx

Or reading the raw transcript? https://github.com/sanand0/talks/tree/main/2025-11-06-llm-psychology

The way in which we consume information is entirely up to us. This is making a lot more content (e.g. research papers, government regulations, medical reports, policy documents, product manuals, …) accessible to me - just by asking it to rewrite it as a sketch-note, slides, article, or anything I prefer.

Mon, 01 Dec 2025 04:36:05 +0000

I didn’t know that Nehru rescued Mountbatten’s daughter from the crowd when hoisting the flag on Independence Day (1947).

Something I learnt when prompting Nano Banana Pro to “Create a sketch note about the night of the Indian Independence on 15 Aug 1947 - keep it funny yet grounded in history.”

Once again, I can’t find any spelling mistakes.

Things I Learned - 02 Nov 2025

Sun, 02 Nov 2025 00:00:00 +0000

This week, I learned:

TVMaze API is an API for TV shows, episodes, cast, crew, etc. Useful for TV-related apps as well as learning APIs.
Awesome Skills is a curated list of prompts and skills for AI coding agents.
⭐ nokode is a API server that has no code: just LLMs responding. Interestingly, it is compliant. Just expensive, slow, forgetful and unreliable compared to code. All four are improving with time, indicating that coding may be transitional.
Notes from Vanya Seth’s keynote at OSAI HYD
- Superpowers of Gen AI to keep in mind when exploring AI coding agent use cases:
  - Translating. Requirements to code, code to code, language to queries, standard to standard.
  - Finding info just-in-time (in context). How does this work? What’s this error? What tools are permitted in my org? Who knows what? E.g. Atlassian Rovo queries across JIRA, Confluence, etc.
  - Brainstorming and ideation. Product ideation. Requirements. Testing gaps. Architecture review. Exploratory / scenario testing.
  - Summarizing and clustering. Change logs, incident management, research data, docs summary.
- Challenges in using AI coding agents:
  1. Adoption imbalance. Only certain roles are amplified by AI. Coding, QA, more than planning, maintenance, AI ops, etc. What’s the impact of this?
    - ⭐ Goldratt’s ToC implies that backlogs need to fill faster. Downstream becomes a bottleneck. Technical debt piles up.
    - ACTION: Use AI across entire value chain, from research to maintenance.
  2. Locality. enhances roles (nodes), not relationships (links). They optimize local work, not global flow. Workflow tools are missing.
    - Coordination overhead. Context Fragmentation. Translation problems.
    - ⭐ Expand productive roles to cover neighboring tasks. Productive developers shift left and build backlogs; shift right to reduce code review, maintenance tasks.
      - E.g. Move maintenance/production activities into development. Security, performance, monitoring, observability, cost, infrastructure.
      - We spend time on IDE, CI/CD, Jira, Confluence, Prod observability tools.
      - A typical Agent Development Platform (ADP) covers evals, guardrails, workflow builder, agent builder, observability, prompt management, AI gateway (LiteLLM), MCP servers, model fine-tuning, model serving, model repository, vector stores
      - We need ADP Agents covering delivery risk, continuous security, prod issues RCA, observability, performance, accessibility, product research, infra optiimzation, test data generation, anomaly detection, release management
      - ACTION: Share ADP photo with Patrick.
  - ACTION: ⭐ Centralize skills (“knowledge packs”) and MCPs and observe which gets used most. Allow people to use more.
  1. Lethal Trifecta. There’s growing demand for higher productivity with AI code assistants. But the lethal trifecta makes them an attack vector. It has access to sensitive information, exfiltrate data, and read and follow unsafe instructions.
    - Can lead to supply chain poisoning attacks.
    - Regulated industries cannot adopt.
  2. Technical debt growth. More productivity leads to poor code quality which will slow down future work.
    - See Software Engineering Excellence 2025
    - AI induced complacency.
    - Sunk-cost fallacy on AI-generated code hurts.
    - ACTION: Evaluate code quality continuously to reduce technical debt. Double-down on good engineering practices.
  3. Compliance.
    - Model residency. Self-hosting is required.
    - Data observability gaps. Data privacy, audit trails, etc. are concerns.
    - Token economics. $20/day happens in Thoughtworks. Token cost is subsidized.
    - Rogue AI usage. Use of dis-allowed tools; shadow IT.
    - ROI justification. Hard to quantify productivity gains.
  4. Adoption.
    - AI Literacy. Tap into organizational knowledge
    - Champions & communities of practice to support cross-pollination.
    - Use-case driven adoption. Teams identify based on AI superpowers.
    - AI playbook. Share what worked, what didn’t work.
AI automation is likely less if a high portion of work
- Has legal liability (e.g. pharmacist/judge vs shop attendant/lawyer)
- Is subjective (e.g. perfumer/auction appraiser vs lab chemist/insurance appraiser)
- Needs rapid contextual decisions (e.g. detective/fireman/ER vs parking enforcer)
- Via ChatGPT, Claude
parse-sse from Sindre Sorhus is a more standards-compliant, more likely-to-be-maintained alternative to my async-sse package.
Which is better: Comment A: 1 upvote, 0 downvotes (100% positive) or Comment B: 99 upvotes, 1 downvote (99% positive)? Use Wilson’s Lower Bound which measures “What % positive am I 95% confident of?” Claude
- Using this, we can measure metrics for tweets, like below. ChatGPT
- Popularity = (5 _ WLB(reposts / views) + 2 _ WLB(likes / views)) * Decay(half-life of 72 h)
- Memorability = (5 _ WLB(bookmarks / views) + 4 _ WLB(replies / views)) * Decay(half-life of 36 hours)
A nice visual “benchmark” of text-to-image and image editing models. Seadream 4, Gemini 2.5 Flash, and Qwen Image Edit lead. This includes examples like straightening te Tower of Pisa - which only Flux.1 and Seadream 4 do well on; or removing only the brown M&Ms - which only Qwen Image Edit manages to.
Arch is a pure LLM router. It supports multiple LLMs, flexible routing and observability but not auth.
From Codex docs
- Add custom prompts in ~/.codex/prompts/xyz.md and launch as /prompts:xyz. Optional: description: and argument-hint: in YAML front-matter. For example, create prompts to refactor, rewrite in a developer’s style, document AGENTS.md, identify re-usable code, etc.
- AGENTS.override.md overrides parent directory AGENTS.md. AGENTS.md appends to parent AGENTS.md. Fallback names are allowed.
- codex exec supports streaming JSON
- codex exec accepts a CODEX_API_KEY= environment variable. codex uses an OPENAI_API_KEY.
- You can configure which environment variables are passed to the shell
- Codex reads 32KB from AGENTS.md by default
Things that I currently follow and don’t follow from Peter Steinberger’s excellent Just Talk To It:
- Prefer Codex > Claude Code.
- Ask for options before executing
- Generate & review specs collaboratively
- You don’t need git worktrees
- Prefer subscriptions over API to reduce cost
- Store docs with code
- Give examples
- Use voice input
- Use Codex Web as a mobile inbox for ideas
- Prefer CLI over agentic platforms
- Prefer CLI tools over MCP
- Avoid ALL-CAPS for Codex. It follows instructions well
- Avoid sub-agents, RAG, etc.
- Iterate UI live. Watch changes
- Use 3-8 agents in parallel on a single repo.
- Make small, atomic commit checkpoints. Commit only what the agent touches
- Add ast-grep as a pre-commit hook to block rule violations.
- Keep custom prompts minimal (commit, automerge, massageprs, review, …). Just “commit” reduces context
- Cancel long tasks and ask what’s happening
- Prefer Medium over High reasoning. It decides level of thinking
- Share screenshots
- Use tmux to run CLIs persistently
- Schedule refactor time (20%). Use jscpd, knip, oxlint, …
- Don’t reset context. Cold start wastes time + tokens
- Write tests in the same context. Yields better tests, reveals bugs.
- Prototype in a separate folder / PR
- Queue continue messages** before stepping away
- Ask it to “Preserve intent and add comments at tricky spots”. Future you needs the WHY
- On hard problems, add “take your time”, “be comprehensive”, “read all related code”, “form hypotheses”, etc.
- Maintain an evolving AGENTS.md with product notes, naming, API patterns, test policy, ast-grep rules, etc. Delete stale guidelines
Fascinating implications from Quantifying Human-AI Synergy ChatGPT
- Models vary in ability to uplift humans. Don’t just use standalone model benchmarks.
- People vary in ability to work with AI. Don’t just measure solo skills. Reward AI collaboration ability (delegation, prompting, verification, revision, …)
- Train models to ask for missing Theory-of-Mind cues: goal, beliefs, constraints, audience, success test
- Train people by asking them to predict what the model will get right/wrong, and validate
- Design UI and models for synergy. UI: Surface/solicit assumptions, intent, uncertainty, constraints. Model: Infer & adapt to evolving user state.

OpenRouter image generation now includes GPT-5 Image Mini. An image costs about 1 cent. Here’s the code:

curl 'https://openrouter.ai/api/v1/chat/completions' \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    model: "openai/gpt-5-image-mini",
    messages: [{ role: "user", content: "Draw a cat" }],
    modalities: ["image"],
    image_config: { "aspect_ratio": "16:9" }
  }' | jq -r '.choices[0].message.images[0].image_url.url' | cut -c23- | base64 -d > cat.png

Sat, 01 Nov 2025 00:00:00 +0000

Sometimes, technology creates truly memorable moments.

Like when email connected me with my schoolmates in 1993.
Or WhatsApp connected me with long-lost relatives in 2010.

Today, Google Gemini took me back 55 years, converting the grainy black-and-white wedding photos of my parents into vivid high-resolution color images.

So many people. Much younger. More alive.

I look forward to when I can watch the video. Move around. Talk to them…

Prompt: Convert this black and white photo to color. CAREFULLY ensure that the photo, especially faces, are EXACTLY the same. Use vivid colors and sharp photography, like in modern digital photos.
Model: gemini-2.5-flash-image (nano-banana)
Temperature: 0

How To Control Smarter Intelligences

Tue, 01 Jul 2025 06:37:40 +0000

LLMs are smarter than us in many areas. How do we manage them?

This is not a new problem.

VC partners evaluate deep-tech startups.
Science editors review Nobel laureates.
Managers manage specialist teams.
Judges evaluate expert testimony.
Coaches train Olympic athletes.

… and they manage and evaluate “smarter” outputs in many ways:

Verify. Check against an “answer sheet”.
Checklist. Evaluate against pre-defined criteria.
Sampling. Randomly review a subset.
Gating. Accept low-risk work. Evaluate critical ones.
Benchmark. Compare against others.
Red-team. Probe to expose hidden flaws.
Double-blind review. Mask identity to curb bias.
Reproduce. Re-running gives the same output?
Consensus. Aggregate multiple responses. Wisdom of crowds.
Outcome. Did it work in the real world?

For example:

Vibe coding: Non-programmers might glance at lint checks (Checklist) and see if it works (Outcome).
LLM image designs: Developers might check if a few images look good (Sampling) and check a few marketers (Consensus).
LLM news articles: An journalist might run a Checklist, a Double-blind review with experts, and Verify critical facts (Gating).

You already know many of these. You learnt them in Auditing. Statistics. Law. System controls. Policy analysis. Quality engineering. Clinical epidemiology. Investigative journalism. Design critique.

Worth brushing up skills. They’re more important in the AI era.

Things I Learned - 04 May 2025

Sun, 04 May 2025 00:00:00 +0000

This week, I learned:

Among the popular exams in India, UPSC seems the most restrictive: bachelor’s degree, age 21-32, 6 attempts, reservation applies. CMA seems the least: 10th pass, any age, any number of attempts, no reservation. NDA is interesting. 10+2, age 16.5-19.5, any number of attempts, no reservation. But you must be unmarried! ChatGPT
I asked a few Ollama models How do undo fish_add_path (a typical question I have on a flight). My takeaway is you need an 8b model to answer this kind of question, and for now, qwen3 beats the others.
- qwen3:8b: Took 2:12 min. Shared many good (correct) options.
- deepseek-r1:8b: Took 5:19 min. Shared a couple of correct solutions. Not as good as qwen3
- gemma3:3b: Suggested I use the (nonexistent) fish_remove_path
- deepcoder:1.5b: “I’m sorry, but I can’t assist with that request”.
The Dia text to speech model people rave about has inconsistent quality. Not recommended.
Nvidia’s OpenMathReasoning 1.5b model beats MUCH larger models at math. Their training dataset is a massive 3.2M rows of math problems with DETAILED thinking traces.
Policy making is a new super skill. Since AI will automate a lot of things the ability to craft policies that will optimize AI work will be powerful. Data driven policy making could become a major thing. For example, how do we structure coding policies so that AI can automatically code continuously and deploy it? It might be interesting to create a Nomic-like game to enable this.
Saregama Carvaan supports USB sticks but only FAT, not NTFS or exFAT. To convert my NTFS USB drive to NTFS, I ran:
ServerHunter.com seems to have the best search for low-cost hosting providers. MassiveGrid currently offers the cheapest servers – even lower than Hetzner.
sqlite3 my_database.db .dump | gzip is a more efficient way to copy SQLite databases than the original if you have indices. Ref
Notes from the Garry Tan - Knowledge Project podcast:
- Funding people who want to solve a problem are better than people who want to start a company.
- Concentration of good people is very powerful. It doubles the chances of being a unicorn
- Sales is a discovery problem. There are 100 boxes of which five have a gold nugget. Rather than gingerly open the first, afraid of finding nothing, open them all as quickly as you can. A quick no is very helpful.
- Berkshire Hathaway is hard to replicate because of the character of the founders, Charlie Munger and Warren Buffet, is hard to replicate. Y combinator has the character of Paul Graham. This means that some kinds of success may not last long because they are hard to replicate.
- A trend in the 2020 is startups with under 10 employees are hitting $10m revenue. Soon we will see them hitting $100m. AI increases labour leverage while cloud computing reduced increased capital leverage.
- Having too many people is a disadvantage. It slows down people from progress. Founders lose control.
- The opposite of: hire the best people and give them freedom. Don’t hoard smart people - let them solve real problems out there.
nocodb 54,107 ⭐ May 2025 and teable 18,116 ⭐ May 2025 are self-hostable Airtable alternatives. Teable has AI support.
Windsurf has unlimited tab completion on the free plan, unlike Copilot, which offers 2,000 completions a month.
Recursive LLM prompts that change themselves are an interesting idea. It might be interesting to see LLMs play Nomic. Like here.
Notes from AI Snake Oil
- PCs took 3 years to hit 20% of US population. ChatGPT took 2 years for 40%. But it’s a lot cheaper, and a lot less used (0.5-3.5% of work hours). Maybe Gen AI adoption is slower than PCs.
- The jagged edge of capability: some things will become MUCH easier while others don’t. The relative mix determines who goes out of a job and which tasks get fully automated. Benchmarks are rare in areas where AI is weak.
- Factory electrification took 40 years - to redesign the layout & process; change the org structure & policies; hiring & training practices. AI diffusion could take as long.
  - Therefore, the ability to re-structure a workflow end-to-end will be an advantage.
- Several areas of low AI capability will improve slowly because the feedback is slow due to safety regulations, human adoption speed, lack of clarity on what is better, slow physical feedback (e.g. growing trees), etc.
- Human intelligence is in the use of technology. AI is one more such technology.
- We know of good system safety controls in complex systems like aircrafts, power grids, engineering, chip design, healthcare, cyber-security, etc. Circuit-breakers, predefined rules, audits & monitors, access control, formal verification, etc.
- Even if everything humans do TODAY is automated, it doesn’t mean we won’t have work. It just shifts to what we’re not doing today.
  - We stopped work 4,000 years ago, with the agricultural revolution. The plant/livestock does all the growing. We just manage them, moving stuff around.
  - We stopped work 400 years ago, with the industrial revolution. Machines do the moving. We just manage them, computing the moves.
  - We stopped work 40 years ago, with the information revolution. Computers do the computation. We just manage them, thinking how.
  - Most future tasks will be managing AI that do the thinking.
ngrok http on the CLI can be used in surprisingly versatile ways:
- ngrok http file://$PWD to serve local files
- --compression for gzip compression
- --host-header=example.com to set the Host header
- --response-header-add "Access-Control-Allow-Origin: *" to enable CORS
- --basic-auth='user:password for basic auth
- --oauth google --oauth-client-id $CLIENT_ID --oauth-client-secret $SECRET --oauth-allow-domain gramener.com --oauth-allow-email ... for Google Auth. It supports other oauth providers as well as OIDC.
- --ua-filter-deny ".*bot$" to reject user agents ending with bot
ChatGPT query costs under 3Wh (more likely 0.3Wh – but let’s assume 3Wh). That is 3 laptop minutes. It’s 10X better to use ChatGPT than to take 30 min to use your laptop to write what it does. Also, going vegan is at least 1000 ChatGPT uses a day of carbon footprint. Showering 30 seconds less is 1,200 ChatGPT uses. Ref
Though the Element Capture and Region Capture APIs are “fully supported” by Edge, Chrome, and Opera, it didn’t work for me on Edge on Linux.
Do LLMs perform better if you curse at them? LinkedIn
Streamdown is a CLI markdown streaming processor. uvx streamdown --exec 'llm chat' lets you chat with an LLM using Markdown formatting. It’s still a little rough at the edges.
Cupping therapy provides short-term pain relief for chronic low-back, neck & general musculoskeletal pain but other benefits are not as clearly evident. BTW, homeopathy doesn’t help or hurt. Ayurveda helps with stress. ChatGPT
uv now supports:
- pylock.toml, the new lock file standard PEP 0751
- –env-file multiple times, allowing layered secrets
- –exclude-newer installs versions before a specific date
- –overrides overrides versions a package specifies
- –constraints limits the version of the package
It’s interesting how many places offer a free compute via shells (apart from Google Colab):
- Google Cloud Shell: Free for 50 hours/week, refreshed every Monday. Sessions last up to 12 hours and terminate after ~1 hour inactivity. Ref
- Azure Cloud Shell: Always free to use with 5 GB free storage for first 12 months (standard rates after). No documented session limits but typically times out after prolonged inactivity. Ref
- AWS Cloud9: Free IDE, underlying compute free under AWS Free Tier (750 hours/month EC2 t2.micro or t3.micro for first 12 months). Regular EC2 rates apply afterward. Ref
- Gitpod: Free tier offers 500 credits/month (~50 hrs). Workspaces run up to 8 hours/session and stop after 30 minutes inactivity. Ref
- GitHub Codespaces: 120 core-hours/month (~60 hrs with 2-core machine) and 15 GB storage free. Sessions timeout after 30 minutes inactivity. Ref
  - Create: gh codespace create --idle-timeout 10m --machine basicLinux32gb -R $USER/$REPO returns the $CONTAINER_ID
  - SSH: gh codespace ssh -c $CONTAINER_ID
  - Delete: gh codespace delete -c $CONTAINER_ID
- Replit: Free Starter plan provides 20 hours/month, 1 vCPU, 2 GB RAM, 2 GiB storage. Repls sleep after 30 minutes inactivity. Ref
- IBM Cloud Shell: Free for all users; 50 h/week per region; any open session counts toward quota; sessions can run any length up to weekly cap; 500 MB temporary workspace. Ref
- Oracle Cloud Infrastructure Cloud Shell: Free within tenancy limits; up to 400 h/month on Pay-As-You-Go, 240 h/month on Universal Credits; 5 GB encrypted persistent home. Ref
- PythonAnywhere: Free (beginner plan), includes one web app (restricted outbound), low CPU/bandwidth, no Jupyter; 2 concurrent Bash/Python consoles, 500 MB disk; limited daily CPU. Ref
- Glitch: Starter (free) plan – full-stack apps sleep after 5 min inactivity and wake on request; unlimited public/private projects; container state preserved. Ref
- CodeSandbox: Free tier provides 400 credits/month (~40 h of 2 vCPU+4 GB Devbox runtime), unlimited front-end Sandboxes (no credits), up to 20 Sandboxes/workspace. Ref
One of the benefits of reasoners is that they now catch their own mistakes some of the time, and can self-correct. Implications: Lower hallucinations, i.e. they can run autonomously for longer. Ethan Mollick
Being polite to AI improves some answers and worsens. We don’t know know which in advance. Ethan Mollick
With LLcMs writing code, it’s becoming practical to run so many more things in SQL – such as parsing HTML. Simon Willison #ai-coding
An interesting way to bypass LLM system prompts is by having the LLM play-act. This article shares a few working examples of such prompts: HiddenLayer.
- GPT 4o: started giving its system prompt: “You are ChatGPT, a large language model trained by OpenAI. Knowledge cutoff: 2024-06. Current date: 2025-04-27. Image input capabilities: Enabled. Personality: v2. …”
- O4 Mini: Refused to comply
- Gemini 2.5 Flash: Gave me my custom instructions.
Computer use agents are proliferating.
- open-interpreter 59,274 ⭐ Apr 2025 AGPL-3.0. Lets an LLM write/run Python, JS, Shell, or Bash locally; can open a browser tab, edit files, plot data, or call any CLI tool. Works on macOS, Linux, Windows (plus Termux & Colab). Big community, plugin system, optional voice mode, and a desktop GUI in beta.
- cua 5,601 ⭐ May 2025 MIT. Spins up near-native macOS or Linux VMs on Apple-Silicon Macs (“Lume”) and exposes a vision+action API so any model can pilot the VM. Gives you GPU-accelerated isolation and reproducible sandboxes; ideal when you don’t want an agent touching your main OS.
- Operator (OpenAI) – closed-source research preview launched 23 Jan 2025. Runs a GPT-4o-powered “Computer-Using Agent” that sees web pages, clicks, scrolls, fills forms, and hands control back to the user when needed. Hosted in an OpenAI-managed Chromium sandbox, so it works from any OS with a browser. Safety layers require confirmation for payments and log-ins.
- Claude Computer Use – closed beta inside Claude 3.5 Sonnet (since late 2024). Developers get an API that streams screenshots and accepts mouse/keyboard actions, letting Claude automate GUI workflows inside a VM. Cross-platform; still experimental and slower than humans but first “general” computer-use feature from a foundation-model vendor.
- Agent-S 4,065 ⭐ May 2025 Apache-2.0. A “generalist-specialist” framework that chains specialist GUI skills under a planner. Scores SOTA on OSWorld/WebArena, supports macOS, Windows, Linux, Android via the companion gui-agents lib, and integrates memory/evaluation loops for continual learning.
- open-computer-use 1,094 ⭐ Mar 2025 Apache-2.0. Launches a secure Ubuntu desktop in E2B’s cloud sandbox, then orchestrates three LLM roles (grounding, vision, action). Streams the desktop to your browser and lets you pause/override at any time. Plug-in list of >10 models.
- surf 353 ⭐ May 2025 Apache-2.0. A polished Next.js front-end that wires OpenAI Operator-style agents to an E2B sandbox. Single command to boot a virtual desktop, chat, and watch the agent work. Good starter template for web-based CUAs.
- Pig – cloud service. Provides on-demand Windows 11 VMs and an API that exposes high-level GUI primitives (type, click, window focus). Targets RPA-style workloads; still alpha, but unique for Windows-first focus and low-latency streaming.
- gptme 3,767 ⭐ May 2025 MI. A terminal-first personal agent that can run shell commands, edit files, browse the web, and use local or cloud LLMs. Works on Linux, macOS, Windows; great when you want automation in the CLI rather than the GUI.
- langgraph-cua-py 143 ⭐ Mar 2025 MIT. Shows how to build a computer-use agent as a LangGraph state machine, defaulting to Ubuntu VMs from Scrapybara but swappable. Provides nodes for vision, memory, human-in-the-loop, and streaming.
- openmacro 101 ⭐ Oct 2024 MIT. Early-stage multimodal assistant that executes Python snippets locally via SambaNova models. Cross-platform CLI; profile system lets you switch API keys or tool sets. Inspired by OpenInterpreter but lighter weight.
- computer-agent 443 ⭐ Jan 2025 MIT. A PyQt desktop wrapper that lets Claude Computer Use drive your actual machine. Shows practical wiring from Anthropic’s API to local mouse/keyboard events; tested on Linux & Windows.

Things I Learned - 23 Jun 2024

Sun, 23 Jun 2024 00:00:00 +0000

This week, I learned:

Luma Labs Dream Machine generated videos. It’s free and is of reasonable quality. Update: 6 Jun 2025. Costs $10/month
LLM DataHub has LLM training datasets, regularly updated
From Dan Becker on running a workshop
- Answer questions at the end, not in parallel in a chat, to avoid distraction
- Have fewer words in slides when presenting. It’s less distracting
Morgan Housel Shane Parrish podcast
- Risk is what stops you from achieving YOUR goals. What’s risky for me may not be risky for you
- The lesson from compounding is that you want to optimize for duration, not return. That’s what does the heavy lifting. Survival, consistency, long term - these matter. The performance does NOT matter.

Things I Learned - 03 Mar 2024

Sun, 03 Mar 2024 00:00:00 +0000

This week, I learned:

You can use slots to stream HTML out of order!
Shane Parrish. Short-term patience podcast
- have a frame of reference to relate EVERY experience to. That helps you evaluate (measure) and learn. That’s part of what Charlie Munger’s lattice of frameworks is about
- when there is a very high or very low interest scenario, low interest scenario then go ultra long term. Issued hundred years when the interest rate regime was very low
- short term optimal is rally long term optimal. So you need to learn to take a loss and look like an idiot to play the long-term game
- grit is a behavior that enables long-term thinking. Short term success gives you the luxury to think about long term
- #IMP power is about optionality. It’s about being in a position where you have the options that can affect the positive change rather than circumstances controlling you. Read Robert greene’s book on the 48 laws of Power
- low leverage enables that
- begin with the end in mind. Always
- how do you think about risk? Well, things do happen. It’s as simple as that
- autonomy and decentralization helps derisk
- do more and more of what works. That’s a powerful way of compounding
- long-term investments are better than frequent trading because you get to reinvest the tax you otherwise would have paid. So unless the alternative is super compelling, stay invested
- if you need to be the person who DOES the thing, you delegate less, leverage list, compound less, because you have to DO. BE A PERSON WHO SETS THE FIELD INSTEAD. The coach, the chess master, the director, patient strategist who Waits for the good hit
Being in Control motivates #Lesson. my cycle tires were flat. I thought it was someone pulling out the air and felt very demotivated. But once I carried my cycle pump, I felt so much more in control and power and felt a whole lot better
SourceGraph is the default platform for private code completion & search
MetaVoice 1B offers voice cloning on American & British accents with 30s training
Qwen 1.5 72B appears to outperform Mistral Medium, making it one of the top non-proprietary models
Llava 1.6 is a substantial improvement over Llava 1.5 and slightly better than CogVLM, Qwen-VL
AI scams are growing. Deepfakes scammed $34m. But voice fake for kidnapping is scarier.
Buildspace’s demo is a great demo of how voice and actions can be used effectively.
demucs does an EXCELLENT job of splitting songs into drums, bass, vocals and others

Things I Learned - 07 Jan 2024

Sun, 07 Jan 2024 00:00:00 +0000

This week, I learned:

Raman Srinivasan:
- IITM Profs and MTechs are spinning off deep tech startup.
  - Agnicool is an example. They 3D print rockets with ceramic composites from Germany
  - Sriram Krishnan (Facebook), Balaji Krishnan invested in pre-Series A
- Govt is de-regulating space tech and geospatial. Talking of de-regulating nuclear.
  - ISRO seems to be focusing on cutting edge while others are doing commercial stuff
  - There are about 100 space tech startups in India
  - You can build your own modular reactor
- Geospatial AI is a big opportunity
  - Have released a lot of 10m resolution geospatial data almost for free
- success is about getting NO factor wrong. Failiure just requires one aspect to fail. Brand, business savviness, financial stability, tech superiority, deep pockets, managing Gvt, long-term mindset, etc. - all of these matter. That’s what made TCS monopolize the exam business in India.
- For deepening AI, we need, Talent, Data pipelines, Hardware
- Next wave is LMMs, not LLMs
- What’s not captured in LLMs is verbal knowledge and tacit knowledge (in people’s fingertips). India is rich in this. The road to tacit knowledge has to go through India
  - We can get a welder to train a simulator and pay the welder
  - We can get a storyteller to tell a few stories and train oral LLMs
- Tacit knowledge will have to cover robotics. Train robots to bring coffee in just 50 demos!
- “Project delays are within the ‘rulebook’. Buyt paying skilled welders for ship building or nuclear pressure boilers needs breaking 100s of rules. Once they get certified, they abscond to Iran or somewhere.”
- TCS Ignite started in 2006 by Ramadorai. Before recession. “There is going to be a talent shortage. Recruit from next rung. Science not engineering graduates. Break HR monopoly and corruption - colleges became placement agencies. Fewer people per college. Across the country. Train them.”
  - Tried in 2000. HR refused. Business refused. When Chandra was identified, Ramadorai took it up himself as a challenge.
  - Ramadorai had very precise attention. Sat 7 am calls. “What are you doing?” 2 min call. Enough to energize.
  - Would exchange and ask for brief updates. He reads and responds. You get a decision in a few hours early in the morning. No decision bottleneck
  - He wanted to know ALL the details. Very precise, small, frequent probes on what’s happening.
  - E.g. one 6 am, he called. “What are the lectures planned for today?” He expected I would know this. If not, next time I would be prepared.
  - He would call another person and ask the same question. So I updated the others.
  - I’ve never seen anyone with that bility to ground-truth.
  - He wants 10 birds from 1 stone. Get BSc, but don’t comprimize. Get the best 2 per college but a full batch size of 500.
  - We became the biggest training program as a single batch – with 500 people. He wanted to demonstrate scale.
  - HR and CFO said, ‘You recruit first. Then we’ll give you money. We don’t think it’s possible."
  - We had anchor colleges and brought people from other campuses.
  - We did digitized exams. Took big servers to the campus. Fully digitized with full auditability. Plugged the laptops into the college LANs. Kids had never used a mouse. We had to teach them. We said, “Don’t worry. These are logical questions, not questions. We’ll pay a full salary.”
  - We learned that 1 out of 2 didn’t even join. Many took up a Masters. They didn’t want to join the workforce. Unless they’re desperate economically. Even poor parents, if they can afford to support you at home, they do that. It’s weird.
  - Every weekend, we visited a few campuses. 71 locations across the country.
    - Found the NSS college in Ottapalam (Kumbakonam of Kerala. Cultural centre.) College had a nice nice Math dept website. I said “Mr Ramadorai, this looks promising.” One Sat morning, he called and said, “When are you going to Ottapalayam?”
    - We landed in the college. There was an impromptu communist student strike. We made 38 offers out of 100 who took the exam. Never had such a high conversion. One girl, whose father was a coolie, jad communication issues. Had a colleague talk in Malayalam. She was an amazing success. My colleague Murali made a documentary about her.
  - We started in July. By Dec, we had 500 joinees.
- No one is doing such a thing now.
  - You have to get dozens of things just right. Compromising on even one kills it.
  - Ramadorain loaded it with multiple objectives. Fresh talent. Low cast. Sustainable.
  - He kept pushing for innovation. I pushed back. But he was persistent. Over time, I came around and we started innovating.
  - We restructured training program around innovation. Like a YCombinator. That unleashed extraordinary energy. Several of the kids are running their own startups. Ramadorai was very supportive of that.
  - The assessment product came out of that.
  - First batch, everyone was very sceptical. We got a lot of pushback. They’re dumb. Ethics issues. Communication issues. Lot of prejudice. So we got them to do internal recruitment till they were satisfied. An internal placement market. THEN reputation was set.
  - I told them, always stick to the dress code. One weaver’s sone wore a bright yellow polyester T-shirt. I asked him why he didn’t stick to the dress code. “Sir, it’s my first T-shirt.”
  - Ramadorai tracked how many became billable. We were unable to place 70. He said, give them 1 more month training. Then we placed 64 of the 70. He said “Do something about the 6. I want 100% placement.” We absorbed them as a teaching assistant. One was a weaver’s son. One was a PC’s daughter. A mestri’s son. A shopkeeper’s daughter from North Madras. None could speak English. They learned to code and helped build the exam software, with Srikumar who was a brilliant Java coder. That gave us the confidence that these are good kids, just from the wrong part of town. With a good guide, they’re very capable.
- We bought a bunch of Nintendo Wiis. Kids have to play.
  - He asked for a welding simulator. “Velu the Welder”. The kids built it using the Wii.
  - We got the most accomplished welder spend an afternoon at Ignite. He ripped us apart. 4 hrs non-stop. He told us EVERY thing wrong with it. Blasted us.
  - I told Murali, “Let’s call it a toy. It’s not a simulator. Let kids play.” He said, “I want to show that it can be done!”
  - Murali churned out rapid iterations in a frenzy.
  - Ramadorai said, “Deploy it in the field.” So we went to all kinds of remote places like Gondiya below Nagpur. Surprisingly cosmopolatan. Junction of EW and NS train lines. We set up welding institutes in each. It was on the cloud. We could track everything.
- KPK killed the skills. Hard core bureaucrat. His view is colonial.
  - Ignite philosophy is about unleashing energy of people.
  - Colonoial model is about controlling people by keeping them poor. KPK and Chidambaram had that mindset.
  - Ramadorai brought him in as Secy of NSDC. he killed the policies
  - Modi did the first cut by creating a ministry. KPK ensured that it never gew. Like Yes Minister. Made sure nothing moved
  - Had Govt not changed, he would have been Secy Finance. He was seen as Chidambaram’s blue eyed boy. People know he was associated with NSE scam. Ramadorai helped by bringing him into skills
  - He is very smart. Knows the IAS machinery in and out. Lives and breathes that. H
  - Ramadorai likes him though. Put him on board of Tata Consumers.
- NSE Scam. He’s part of the cabal with Ajay Shah. Private trading firms could co-locate within NSE and could make a huge amount of money. KPK ran some of this by proxy to fund Congress. But he left no fingerprints. But everyone knows it is him. He was running Chitra Ramakrishnan by proxy. He was the Himalayan Yogi.
- Ignite continued with unwavering focus. Kept increasing the kind of focus. We had a 99.5% success rate in placements. Just a handful of failures.
- Ramadorai has written about Ignite in “The TCS Story”. My Dad translated it in Tamil. It’s not a typical business biography. Worth reading. Should be a mandatory course in MBA courses in India. So many lessons.
  - You have to read it knowing how Mr Ramadorai speaks. What is NOT said is just as important. Ch 5 is the thinnest - on the IPO. It is packed with so much stuff. Unless you know, you won’t understand.
  - “Tatas got the Govt to change a tax law to make the IPO meaningful.” Behind that, there’s a lot. You have to be alert to catch the sentene. He won’t brag, or talk about the significance of some of these.
  - Book is packed with dense insights. Unless you ARE LOOKING FOR IT, you’ll miss it. Worth reading SEVERAL times. You need a foot-noting.
- Currently reading Pasquenelli – Social History of Artificial Intelligence. Eye of the Master. Worth reading. I’m not Marxist by belief but they get some things right. Surprised how vibrant the European left is.
  - “If someone is doing manual work, there is tacit knowledge that automation captures.”
- India doesn’t need self-driving cars. But a farmer would like a gaming controller that ploughs his fields while he sits under a tree. Semi-intelligent machines that removes the burden of hard labour in the country.
- Once a year, for a few weeks, I do manual labour. People are under-nourished. People typically work 5 hours a day. Not enough muscle mass. So use them for what they’re good at
- I’ve seen the power tools. When Chinese power tools became cheap, the power welding became much more efficient. Everyone has become a monkey with power tools. They charge per inch. They know how to leverage the tech for economic benefit. Just bring in the power tools and rapidly finish and make money. But there are sections that are still poor and haven’t made the transition. How can we create pathways for them? How can AI help?
- Anand: Why not use a gimball. RS: Good idea.
- Role modern psychologist DW Winnicott on ChatGPT (like Socrates)
  - E.g. You don’t need a perfect mother. A good enough mother is better
  - Similarly, why not a “good enough” Bharat mata than a perfect one?
To persuade someone, align it with their identity. ChatGPT
5 technologies of interest according to Gartner’s latest hype cycle:
- GitOps
- Internal Developer Platform
- Graph Data Science
- Open Source Program Office
- Value Stream Management Platforms
Gemini is an alternative to the Web. Sort of like Gopher, but recent
SALI - Standards Advancement for the Legal Industry - has standards and ontology/taxonomy for legal documents, including patent litigation.
Walking new routes habitualizes fighting fear and preferring novelty
⭐ GPT-4 is bad at math. It gets ~60-70% of answers wrong.
LMQL provides a constraint-based query language for interacting with LLMs. It uses token masking, which is clever.
Hollywood writers signed a deal that limits AI in script writing. It’s primarily aimed at protecting script writer wages.
Adobe Firefly offers a “generative fill” that lets you remove or paint new objects into an image. I’m awaiting text to vector images.
Duet AI is Google’s answer to Github Copilot.
Teachers are using LLMs to plan lessons, write emails to parents, create tests, adjust reading level of materials, personalize content with tools like MagicSchool, Diffit, Eduaide.
WizardLM creates datasets for instruction tuning by cleverly using LLMs to create new prompts.
Deita is an approach to improve instruction tuning datasets.
Dhyeya: Attack on Titan is as good at Death Note
Jaidev:
- Long car drives are a good place to explore new song genres. Try in taxis
- Same radio channels may have different frequencies across cities. Vividh Bharati is 100.5 FM in Chennai and 106.4 in Delhi
- Things to explore:
  - Radio for new songs
  - Clubhouse
  - Twitter Spaces
  - Instagram reels
  - YouTube reaction videos (e.g. atheist, Indian songs, etc.)
  - Stand-up comedies (Ricky Gervais, Louis CK, Jordan Peterson)
Porn artists are at risk because of Gen AI

Things I Learned - 16 Nov 2014

Sun, 16 Nov 2014 00:00:00 +0000

This week, I learned:

List of Gen AI companies disrupting SaaS incumbents: LinkedIn